November 2025 arXiv papers — page 136

Felix Biertümpfel, Jungbae Chun, Peter Seiler

This paper presents a novel approach for ensuring safe operation of systems subject to input nonlinearities and time-varying safety constraints. We extend the time-varying barrier function framework to address time-varying safety constraints and explicitly account for control-dependent nonlinearities at the plant input. Guaranteed bounds on the input-output

Pablo Ruiz-Morales, Dries Vanoost, Davy Pissoort, Mathias Verbeke

Joint-Embedding Predictive Architectures (JEPAs), a powerful class of self-supervised models, exhibit an unexplained ability to cluster time-series data by their underlying dynamical regimes. We propose a novel theoretical explanation for this phenomenon, hypothesizing that JEPA's predictive objective implicitly drives it to learn the invariant subspace of t

Lee-Peng Teo

For a curve $\boldsymbol{\gamma}:I\to\mathbb{R}^n$ of order $n-1$, we prove that the generalized curvatures $\kappa_1, \ldots, \kappa_{n-1}$ can be expressed in terms of the leading principal minors of the matrix $\mathbf{A}(t)^T\mathbf{A}(t)$, where $\mathbf{A}(t)$ is the $n\times n$ matrix whose $i$-th column is $\boldsymbol{\gamma}^{(i)}(t)$. This gives a

P. Sau, N. Hancock, I. Tzoka, V. Khichar

We demonstrate a zero-bias ultraviolet (UV) detector using zinc oxide (ZnO) thin films as the active semiconductor layer, specifically for application in cryogenic conditions. The zero-bias device utilizes the piezoelectric potential developed through interfacial stress in the active semiconductor layer for charge transport. We explored two vertically stacke

Nikolay Blagoev, Oğuzhan Ersoy, Lydia Yiyu Chen

Group Relative Policy Optimization (GRPO) has demonstrated wide adoption in the post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and preferred behaviour is learnt via reinforcement learning. Owing to the small communication volume, GRPO is inherently suitable for decentralised training as the prompts can be concurrent

Max Kreider, John Harlim, Daning Huang

Symmetry in differential equations reveals invariances and offers a powerful means to reduce model complexity. Lie group analysis characterizes these symmetries through infinitesimal generators, which provide a local, linear criterion for invariance. However, identifying Lie symmetries directly from scattered data, without explicit knowledge of the governing

P. D. Marinos, T. A. Porter, G. P. Rowell, I. V. Moskalenko

We use the GALPROP cosmic ray (CR) propagation framework to model the diffuse neutrino and gamma-ray emissions from the Galaxy. A collection of realistic bounding models are developed and predictions of the resulting neutrino and gamma-ray signals are compared to the IceCube and LHAASO data up to PeV energies. We find that all the GALPROP models are consiste

Ramesh Adhikari, Costas Busch, Pavan Poudel

Transaction scheduling is crucial to efficiently allocate shared resources in a conflict-free manner in distributed systems. We investigate the efficient scheduling of transactions in a network of fog-cloud computing model, where transactions and their associated shared objects can move within the network. The schedule may require objects to move to transact

Dilli Prasad Sharma, Xiaowei Sun, Liang Xue, Xiaodong Lin

The widespread integration of Artificial Intelligence of Things (AIoT) in smart home environments has amplified the demand for transparent and interpretable machine learning models. To foster user trust and comply with emerging regulatory frameworks, the Explainable AI (XAI) methods, particularly post-hoc techniques such as SHapley Additive exPlanations (SHA

Jonathan Broadbent, Michael Bailey, Mingxuan Li, Abhishek Paul

Accurate prediction of small molecule solubility using material-sparing approaches is critical for accelerating synthesis and process optimization, yet experimental measurement is costly and many learning approaches either depend on quantumderived descriptors or offer limited interpretability. We introduce Solvaformer, a geometry-aware graph transformer that

Mahdi Samaee, Mehran Yazdi, Daniel Massicotte

Automated sleep stage classification from polysomnography remains limited by the lack of expressive temporal hierarchies, challenges in multimodal EEG and EOG fusion, and the limited interpretability of deep learning models. We propose NeuroLingua, a language-inspired framework that conceptualizes sleep as a structured physiological language. Each 30-second

John Brownfield

We prove that for $\omega: \mathbb{R}^2 \to [0,1]$ sharing the same total vorticity and center of vorticity as the Rankine vortex, the $L^1$ deviation from the Rankine patch can be bounded by a function of the pseudo-energy deviation and the angular momentum of $\omega$. In the case of $m-$fold symmetry, the dependence on the angular momentum can be dropped.

Yu Wing Joshua Lee, Yuanming Wang, Manisha Caleb, Tara Murphy

Long-period radio transients (LPTs) are a recently identified phenomenon that challenge our current understanding of compact objects and coherent radio emission mechanisms. These objects emit radio pulses similar to those of pulsars, but at much longer periods -- on the order of minutes to hours. With duty cycles of only a few percent, individual pulses have

Aaron Miller, Sahil Kommalapati, Robert Moser, Petros Koumoutsakos

Accurate and generalizable Reynolds-averaged Navier-Stokes (RANS) models for turbulent flows rely on effective closures, but currently available closures are notoriously unreliable. Kassinos et al. (J. Fluid Mechanics, 428, pp. 213-248, 2001) hypothesized that this unreliability of RANS models was due to an insufficient description of the statistical state o

Rik Adriaensen, Lucas Van Praet, Jessa Bekker, Robin Manhaeve

Operationalizing definitions of fairness is difficult in practice, as multiple definitions can be incompatible while each being arguably desirable. Instead, it may be easier to directly describe algorithmic bias through ad-hoc assumptions specific to a particular real-world task, e.g., based on background information on systemic biases in its context. Such a

Alessandro V. M. Oliveira

This paper presents and discusses forms of estimation by regularized regression and model selection using the LASSO method - Least Absolute Shrinkage and Selection Operator. LASSO is recognized as one of the main supervised learning methods applied to high-dimensional econometrics, allowing work with large volumes of data and multiple correlated controls. Co

Michael Dang'ana, Yuqiu Zhang, Hans-Arno Jacobsen

Resource orchestration and configuration parameter search are key concerns for container-based infrastructure in cloud data centers. Large configuration search space and cloud uncertainties are often mitigated using contextual bandit techniques for resource orchestration including the state-of-the-art Drone orchestrator. Complexity in the cloud provider envi

Sanjana Cheerla, Vaibhav Garg, Saikath Bhattacharya, Munindar P. Singh

Viewing social apps as sociotechnical systems makes clear that they are not mere pieces of technology but mediate human interaction and may unintentionally enable harmful behaviors like online harassment. As more users interact through social apps, instances of harassment increase. We observed that app reviews often describe harassment. Accordingly, we built

Zag ElSayed, Grace Westerkamp, Jack Yanchen Liu, Ernest Pedapati

Neurodevelopmental disorders such as Fragile X Syndrome (FXS) and Autism Spectrum Disorder (ASD) are characterized by disrupted cortical oscillatory activity, particularly in the alpha and gamma frequency bands. These abnormalities are linked to deficits in attention, sensory processing, and cognitive function. In this work, we present an adaptive machine le

Frank Gilson

Goal. We analyze when the Partition Principle ($\mathsf{PP}$) holds without $\mathsf{AC}$ in models arising from a free finite $H$-action on Cantor space, and reconcile two standard routes to such models. Approach. Route I proceeds via a Boolean-valued presentation $\mathrm{Sh}(\mathbb{B})$ and symmetric names; Route II uses direct forcing with $\mathrm{Fn}(

Guy Blanc, Yizhi Huang, Tal Malkin, Rocco A. Servedio

We consider the relative abilities and limitations of computationally efficient algorithms for learning in the presence of noise, under two well-studied and challenging adversarial noise models for learning Boolean functions: malicious noise, in which an adversary can arbitrarily corrupt a random subset of examples given to the learner; and nasty noise, in w

Victoria M. Ravel, Sarah R. Evans, Samantha K. Holmes, James L. Doherty

The performance and scalability of two-dimensional (2D) field-effect transistors (FETs) are strongly influenced by geometry-defined electrostatics. In most 2D FET studies, the gate overlaps with the source and drain electrodes, allowing the gate potential to modulate the 2D semiconductor underneath the electrodes and ultimately effect carrier transport at th

Marco Musacchio, Alexander P. Antonov, Hartmut Löwen, Lorenzo Caprini

We study a crystal composed of active units governed by self-alignment and chirality. The first mechanism acts as an effective torque that aligns the particle orientation with its velocity, while the second drives individual particles along circular orbits. We find that even a weak degree of chirality, when coupled with self-alignment, induces collective mot

Huixiang Zhang, Mahzabeen Emu

Constructing realistic digital twins for applications such as training autonomous driving models requires the efficient allocation of real-world data, yet data sovereignty regulations present a major challenge. To address this, we tackle the optimization problem faced by metaverse service providers (MSPs) responsible for allocating geographically constrained

Borna Bateni, Yubai Yuan, Qi Xu, Annie Qu

We study distributional transportability of treatment effects in a ``cross-site, one-armed target" design, where both treated and control units are observed in a source site, but only control units are observed in a target site. Our object of interest is not estimating the average treatment effect, but recovering the full treated distribution in the targ

Andrew G. Yates, Jordan Cotler, Nishad Maskara, Mikhail D. Lukin

We demonstrate that in quantum many-body systems, local arrows of time can differ from the global time $t$ induced by Hamiltonian evolution. That is, within a quantum many-body system, the flow of time can be relative to each observer or by proxy each local subsystem. We provide a definition of local arrows of time in quantum many-body systems, and explain t

David Carroll, ChaoChao Dun

Advances in the development of flexible piezoelectric and thermoelectric materials have provided an important avenue for the exploration of energy scavenging through the thermodynamic-coupling of orthogonal energy-scavenging modalities. This has led to a body of work creating hybrid thermo/piezo-electric generator devices (T/PEGs) in which the two effects be

Mikhail Andreev, Alexander Shen

In this paper we provide an easy proof of Barmpalias--Lewis-Pye result saying that all computable increasing sequences converging to random reals converge with the same speed (up to a $c+o(1)$ factor) by noting that it immediately follows from Bishop's upcrossing inequality. We also provide a simple derivation of this inequality.

Yinan Liu, Joseph P. Briggs, Sam Saiter, Meenakshi Singh

While Si clathrates have been explored as promising direct bandgap semiconductors, their practical optoelectronic performance has been limited by high doping levels and structural defects. Hydrogen has long been used to improve the optoelectronic quality of conventional Si, yet its role in clathrate structures remains unexplored. In this study, we demonstrat

Ngaiming Kwok

In the article (Kudela, 2022), experimental demonstrations indicated that two Bio-/Nature inspired optimization algorithms (BNIOAs), Sooty Tern Optimization Algorithm (STOA) and Tunicate Swarm Algorithm (TSA), exhibit a zero-bias, leading to the conclusion that the claims made in the original papers were overstated. In this work, we extend the analysis by in

Sarthak Khanna, Armin Berger, Muskaan Chopra, David Berghaus

Financial markets are inherently non-stationary: structural breaks and macroeconomic regime shifts often cause forecasting models to fail when deployed out of distribution (OOD). Conventional multimodal approaches that simply fuse numerical indicators and textual sentiment rarely adapt to such shifts. We introduce macro-contextual retrieval, a retrieval-augm

Nicolas Venkovic, Hartwig Anzt

This work is motivated by symmetric positive definite (SPD) matrices for which the best sparse approximate inverse (SPAI) with the prescribed nonzero pattern of $A^k$ for some moderate value of $k$, e.g., 1, 2, 3, or 4, fails to capture essential features of the inverse, such as definiteness. In this context, we consider short-recurrence iterative methods fo

Bhabani Prasad Mandal, Sumit Kumar Rai, Ronaldo Thibes

With a recent revival, novel features of the FLPR model have been reported in the literature. A connection between those features to QCD involving the Gribov problem is explored. We investigate the FLPR model in a recently proposed framework of BRST-related symmetries and perform its full functional quantization as a gauge invariant system taking into accoun

Jan Zemen, František Máca, Václav Drchal, Martin Veis

Antiphase boundaries (APBs) are ubiquitous in ordered Heusler alloys and strongly influence magnetic coercivity in Ni-Mn-Ga, yet the link between their atomic-scale exchange interactions and micrometer-scale magnetic contrast measured by magnetic force microscopy (MFM) remains unclear. We combine density functional theory (DFT) and finite-element magnetostat

Hannah Banks, John Carlton, Benjamin Elder, Thomas Hird

Screened scalars are ubiquitous in many dark-sector models. They give rise to non-trivial fifth forces whilst evading experimental constraints through density-dependent screening mechanisms. We propose equipping a 10\,m-scale long-baseline atom interferometer with an annular planar source mass inside the vacuum chamber to search for such screened fifth force

Mahsa Mitcheff, Siamul Karim Khan, Adam Czajka

Developing reliable iris recognition and presentation attack detection methods requires diverse datasets that capture realistic variations in iris features and a wide spectrum of anomalies. Because of the rich texture of iris images, which spans a wide range of spatial frequencies, synthesizing same-identity iris images while controlling specific attributes

Muskaan Chopra, Lorenz Sparrenberg, Sarthak Khanna, Rafet Sifa

Large Language Models (LLMs) excel at evaluating machine translation (MT), but their scale and cost hinder deployment on edge devices and in privacy-sensitive workflows. We ask: how small can you get while still detecting meaning-altering translation errors? Focusing on English->German Critical Error Detection (CED), we benchmark sub-2B models (LFM2-350M, Qw

Justin Cooke, Kathleen Donohue, Clark D Rowley, Prasad G Thoppil

Present forecasting efforts rely on assimilation techniques that adjust the model basic state, meaning that profiles of temperature and salinity are used as measured or converted to temperature and salinity through statistical relationships. This information influences the upper ocean ( $< 1000$ m depth), while minimally influencing the deep ocean. Neverthel

Ludovica Guarneri, Qitong Li, Thomas Bauer, Jung-Hwan Song

Monolayer 2D semiconductors, such as WS2, exhibit uniquely strong light-matter interactions due to exciton resonances that enable atomically-thin optical elements. Similar to geometry-dependent plasmon and Mie resonances, these intrinsic material resonances offer coherent and tunable light scattering. Thus far, the impact of the excitons temporal dynamics on

Aleksei Panov, Aleksei Khovanskii

We discuss here the history of the rhombocuboctahedron and pseudorombocuboctahedron and describe the structure of their paper models. It turns out that one of them serves as an excellent basis for creating a Jack-o'-lantern. The note is written primarily for school students.

Henry Ando, Geyue Cai, Cheng Chin, Tilman Enss

We describe recent theoretical and experimental developments on mediated interactions in mixtures of bosonic and fermionic atoms. We discuss how particle-hole excitations of a Fermi sea can induce long-range interactions between heavy impurities or atoms in a Bose-Einstein condensate. Conversely, phonon excitations of a Bose-Einstein condensate induce intera

Daniel Hwang, Juliet Whidden, Josephine Yu

We show that when integral polytopes are deformed while keeping the same facet normal vectors, the coefficients of weighted Ehrhart and $h^*$-polynomials are piecewise polynomial functions in the ``right hand sides'' of the linear inequalities defining the polytopes. We give an algorithm and an implementation in SageMath for computing these polynomials for s

Julio Carlos Bertua Marasca, Homer Davila Gutierrez, Josue Ismael Mosquera Hadatty

We present an analysis of atmospheric pressure variability inside Jezero Crater on Mars based on measurements from the MEDA meteorological station aboard NASA's Perseverance rover. Pressure data from Sols 182, 361, 504, and 658 reveal seasonal and diurnal fluctuations linked to solar insolation, CO2 condensation and sublimation cycles, and local geomorpholog

M. F. Araujo de Resende

In this paper, we briefly review the Hamiltonian formulation of classical systems that are constrained to submanifolds so that, within this context, the true meaning of classical gauge theories becomes clear. Please note that this paper is nothing more than a near-literal translation of Ref. [1], which we originally published in Brazilian Portuguese in 2018.

Sourav De, Koustav Chowdhury, Bibhabasu Mandal, Sagar Ghosh

Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like k-means and its wide family of variants are still widely used, all of them require the number of clusters k to be supplied as input, and many are notably sensitive to initialization. Conv

Frank Li, Theo Dapamede, Mohammadreza Chavoshi, Young Seok Jeon

Foundation models (FMs) promise to generalize medical imaging, but their effectiveness varies. It remains unclear how pre-training domain (medical vs. general), paradigm (e.g., text-guided), and architecture influence embedding quality, hindering the selection of optimal encoders for specific radiology tasks. To address this, we evaluate vision encoders from

Yuankai He, Weisong Shi

CAR-Scenes is a frame-level dataset for autonomous driving that enables training and evaluation of vision-language models (VLMs) for interpretable, scene-level understanding. We annotate 5,192 images drawn from Argoverse 1, Cityscapes, KITTI, and nuScenes using a 28-key category/sub-category knowledge base covering environment, road geometry, background-vehi

Houming Wu, Ling Chen

Training large language models (LLMs) is fundamentally constrained by limited device memory and costly inter-device communication. Although pipeline parallelism alleviates memory pressure by partitioning models across devices, it incurs activation communication overhead that scales linearly with sequence length, limiting efficiency in long-context training.

Filip Beránek, Václav Diviš, Ivan Gruber

Soiling detection for automotive cameras is a crucial part of advanced driver assistance systems to make them more robust to external conditions like weather, dust, etc. In this paper, we regard the soiling detection as a semantic segmentation problem. We provide a comprehensive comparison of popular segmentation methods and show their superiority in perform

Alexey A Petrov

CP violation, which involves breaking the combined charge-conjugation (C) and parity (P) symmetries, is essential for understanding the observed matter-antimatter asymmetry in the universe. It is a key feature of the Standard Model, originating from complex phases in the Cabibbo-Kobayashi-Maskawa quark mixing matrix. Despite the successes of the SM, the amou

Sabrina DeSoto, JenniferL. Hoffman, G. Grant Williams, Christopher Bilinski

We present six epochs of optical spectropolarimetric observations of the unique and slow-evolving Type Ib supernova (SN) 2012au, between 0 and 295 days post R-band maximum. The polarization levels seen throughout our observations are on average 0.87% +/- 0.05% higher than those of any Type Ib SN~yet studied, suggesting either that it is the most asymmetric o

C. LeMay, A. Lane, J. Seales, M. Winstead

Our research investigates how Natural Language Processing (NLP) can be used to extract main topics from a larger corpus of written data, as applied to the case of identifying signaling themes in Presidential Directives (PDs) from the Reagan through Clinton administrations. Analysts and NLP both identified relevant documents, demonstrating the potential utili

Bram Grooten, Patrick MacAlpine, Kaushik Subramanian, Peter Stone

Generalization to unseen environments is a significant challenge in the field of robotics and control. In this work, we focus on contextual reinforcement learning, where agents act within environments with varying contexts, such as self-driving cars or quadrupedal robots that need to operate in different terrains or weather conditions than they were trained

Joana Tirana, Dimitra Tsigkari, David Solans Noguero, Nicolas Kourtellis

In Split Federated Learning (SFL), the clients collaboratively train a model with the help of a server by splitting the model into two parts. Part-1 is trained locally at each client and aggregated by the aggregator at the end of each round. Part-2 is trained at a server that sequentially processes the intermediate activations received from each client. We s

Ahmed Alia, Mohcine Chraibi, Armin Seyfried

In dynamic and crowded environments, realistic pedestrian trajectory prediction remains a challenging task due to the complex nature of human motion and the mutual influences among individuals. Deep learning models have recently achieved promising results by implicitly learning such patterns from 2D trajectory data. However, most approaches treat pedestrians

Huanhuan Zhao, Connor Vernachio, Laxmi Bhurtel, Wooin Yang

Microscopy such as Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) are essential tools in material imaging at micro- and nanoscale resolutions to extract physical knowledge and materials structure-property relationships. However, tuning microscopy controls (e.g. scanning speed, current setpoint, tip b

Theosamuele Signor, Paula Jofré, Hernan Lira, Sara Vitali

Chemical abundance determinations from stellar spectra are challenged by observational noise, limitations in stellar models, and departures from simplifying assumptions. While traditional and supervised machine learning methods have made remarkable progress in estimating atmospheric parameters and chemical compositions within existing physical models, these

Perrin E. Ruth, Maria K. Cameron

Analysis of complex networks, particularly material networks such as the carbon skeleton of hydrocarbons generated in hydrocarbon pyrolysis in carbon-rich systems, is essential for effectively describing, modeling, and predicting their features. An important and the most challenging part of this analysis is the extraction and effective description of cycles,

Bernardo Perrone Ribeiro, Jana Faganeli Pucer

Radar-based precipitation nowcasting, the task of forecasting short-term precipitation fields from previous radar images, is a critical problem for flood risk management and decision-making. While deep learning has substantially advanced this field, two challenges remain fundamental: the uncertainty of atmospheric dynamics and the efficient modeling of high-

Juliane H. Fuglsbjerg, Peter Reinholdt, Erik Kjellgren, Phillip W. K. Jensen

We present a quantum linear response (qLR) approach within an active-space framework for computing indirect nuclear spin-spin coupling constants, a key ingredient in NMR spectra predictions. The method employs the unitary coupled cluster (UCC) ansatz and its orbital-optimized variant (ooUCC), both suitable for quantum computing implementations, to evaluate s

Qian-Ze Zhu, Paul Raccuglia, Michael P. Brenner

Solving partial differential equations (PDEs) can be prohibitively expensive using traditional numerical methods. Deep learning-based surrogate models typically specialize in a single PDE with fixed parameters. We present a framework for equation-aware emulation that generalizes to unseen PDEs, conditioning a neural model on a vector encoding representing th

Mohammad Mahabubur Rahman, Deepanshu Verma

Due to its nonlinearity, bi-harmonic dissipation, and backward heat-like term in the absence of a divergence-free condition, the $2$-D/$3$-D Kuramoto-Sivashinsky equation poses significant challenges for both mathematical analysis and numerical approximation. These difficulties motivate the development of methods that blend classical analysis with numerical

Stelios Zarifis, Ioannis Chalkiadakis, Artemis Chardouveli, Vasiliki Moutzouri

Inspired by infant development, we propose a Reinforcement Learning (RL) framework for autonomous self-exploration in a robotic agent, Baby Sophia, using the BabyBench simulation environment. The agent learns self-touch and hand regard behaviors through intrinsic rewards that mimic an infant's curiosity-driven exploration of its own body. For self-touch, hig

Mingjie Tan

We prove that the non-covered set in Dvortezky random covering is a set of multiplicity, by showing that the natural multiplicative chaotic measure is a Rajchman measure.

Craig Michoski, Matthew Waller, Brian Sammuli, Zeyu Li

Fusion energy research increasingly depends on the ability to integrate heterogeneous, multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulations. The sheer volume and complexity of these datasets demand the development of new tools capable of systematically harmonizing and extracting knowledge across diverse modalities

Yunqian Cheng, Benjamin Princen, Roberto Manduchi

Indoor localization in GPS-denied environments is crucial for applications like emergency response and assistive navigation. Vision-based methods such as PALMS enable infrastructure-free localization using only a floor plan and a stationary scan, but are limited by the short range of smartphone LiDAR and ambiguity in indoor layouts. We propose PALMS$+$, a mo

Balachandra Devarangadi Sunil, Rakshith Venkatesh, Shantanu Todmal

This study enhances a crowd density estimation algorithm originally designed for image-based analysis by adapting it for video-based scenarios. The proposed method integrates a denoising probabilistic model that utilizes diffusion processes to generate high-quality crowd density maps. To improve accuracy, narrow Gaussian kernels are employed, and multiple de

Sujay Nair, Evan Coleman, Sherrie Wang, Elsa Olivetti

Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challenging. Inspired by recent progress in generative modeling, we develop a learning method which infers the locations of minerals by masking and infilling geospatial maps of resource ava

Haoran Wu

This paper extends the analytical study of the incompressible Euler equations from the classical spherical setting to the more realistic geometry of a biaxial ellipsoid. Motivated by the work of Cheng and Mahalov on fast rotating spheres and Xu on Rossby-Haurwitz solutions on ellipsoids, we adapt their framework to establish a parallel result for Euler flows

Victoria Bonidie, Marshall C. Johnson, Ji Wang, Sydney Petz

We present five datasets of high-resolution optical emission spectra of the ultra-hot Jupiter KELT-20 b with the PEPSI spectrograph. Using a Bayesian retrieval framework, we constrain its dayside pressure-temperature profile and abundances of Fe, Ni, and Ca, providing the first measurements for Ni and Ca for KELT-20 b in emission. We retrieve the pre- and po

E. Lacchin, M. Donati, F. Calura, C. Nipoti

Through 3D hydrodynamical simulations, we explore the impact of Type Ia supernova (SN) explosions on the star formation history and chemical properties of second-generation (SG) stars in young globular clusters with masses of 10^5-10^6 Msun. We assume that the SG is formed out of the asymptotic giant branch (AGB) ejecta of first-generation stars plus pristin

Irma Avdic, Yuchen Wang, Michael Rose, Lillian I. Payne Torres

Quantum state tomography is a fundamental task in quantum information science, enabling detailed characterization of correlations, entanglement, and electronic structure in quantum systems. However, its exponential measurement and computational demands limit scalability, motivating efficient alternatives such as classical shadows, which enable accurate predi

Arman Zarei, Samyadeep Basu, Mobina Pournemat, Sayan Nag

Instruction-based image editing models have recently achieved impressive performance, enabling complex edits to an input image from a multi-instruction prompt. However, these models apply each instruction in the prompt with a fixed strength, limiting the user's ability to precisely and continuously control the intensity of individual edits. We introduce Slid

Erica Flapan, Hugh Howards

We prove that all $1$-vertex spatial graphs with adequate diagrams have minimal crossing number, and that spatial graph diagrams obtained by replacing vertices and edges of a planar embedded graph by minimal crossing link or spatial graph diagrams have minimal crossing number. Finally, we give an example in answer to a question of Adams et al. about minimal

Varatharaja Nallathambi, Aneeta Jose Puthussery, Andrea M. Mingers, Robert Stuckert

Low-cost transition metal high-entropy nanoalloys are emerging as sustainable alternatives to platinum group electrocatalysts. Synthesis conditions of single-phase solid solutions can alter phase stability, causing surface composition changes that affect electrocatalytic performance. Here, we propose to exploit the metastability of carbon-doped Cantor alloy-

Sarath Shekkizhar, Romain Cosentino, Adam Earle, Silvio Savarese

As large language model (LLM) based agents interact autonomously with one another, a new class of failures emerges that cannot be predicted from single agent performance: behavioral drifts in agent-agent conversations (AxA). Unlike human-agent interactions, where humans ground and steer conversations, AxA lacks such stabilizing signals, making these failures

Marisa Hudspeth, Patrick J. Burns, Brendan O'Connor

Tokenization is a critical component of language model pretraining, yet standard tokenization methods often prioritize information-theoretical goals like high compression and low fertility rather than linguistic goals like morphological alignment. In fact, they have been shown to be suboptimal for morphologically rich languages, where tokenization quality di

Marco Angioli, Christopher J. Kymn, Antonello Rosato, Amy Loutfi

The modular composite representation (MCR) is a computing model that represents information with high-dimensional integer vectors using modular arithmetic. Originally proposed as a generalization of the binary spatter code model, it aims to provide higher representational power while remaining a lighter alternative to models requiring high-precision componen

Ajaykrishnan E S, Robert Ganian, Daniel Lokshtanov, Vaishali Surianarayanan

A graph $G$ is a circle graph if it is an intersection graph of chords of a unit circle. We give an algorithm that takes as input an $n$ vertex circle graph $G$, runs in time at most $n^{O(\log n)}$ and finds a proper $3$-coloring of $G$, if one exists. As a consequence we obtain an algorithm with the same running time to determine whether a given ordered gr

Muhammad Yarahmadi, Amin Salehi

We present a Physics-Informed Neural Network (PINN) framework for reconstructing the redshift-dependent Hubble parameter \(H(z)\) within the Tsallis Holographic Dark Energy (THDE) model extended by massive neutrinos. In this approach, the modified Friedmann equation is incorporated into the neural network loss function, enabling training on Cosmic Chronomete

Arash Azizi

We introduce the Two-Mode Janus State (TMJS), a non-Gaussian quantum state defined as a coherent superposition of two distinct Two-Mode Squeezed States (TMSS). This construction serves as a direct, non-Gaussian generalization of the canonical thermofield double (TFD) state, which is itself a single, Gaussian TMSS. We develop a complete analytical framework f

Katie Matton, Purvaja Balaji, Hamzeh Ghasemzadeh, Jameson C. Cooper

Phonotrauma refers to vocal fold tissue damage resulting from exposure to forces during voicing. It occurs on a continuum from mild to severe, and treatment options can vary based on severity. Assessment of severity involves a clinician's expert judgment, which is costly and can vary widely in reliability. In this work, we present the first method for automa

Dylan Possamaï, Mehdi Talbi

This paper focuses on the optimal control of a class of stochastic Volterra integral equations. Here the coefficients are regular and not assumed to be of convolution type. We show that, under mild regularity assumptions, these equations can be lifted in a Sobolev space, whose Hilbertian structure allows us to attack the problem through a dynamic programming

Warren Li, Yiqian Wang, Zihan Wang, Jingbo Shang

In-context learning (ICL) enables large language models to perform new tasks by conditioning on a sequence of examples. Most prior work reasonably and intuitively assumes that which examples are chosen has a far greater effect on performance than how those examples are ordered, leading to a focus on example selection. We revisit this assumption and conduct a

Gabriel Larotonda, Iván Rey

We show that a Lie group $G$ admitting a bi-invariant distance must be the product $G=H\times K$ of an abelian group $H$ and a compact group $K$ with discrete center. Moreover, the distance in $G$ must come from the infima of lengths of paths for a unique infinitesimal metric (a Finsler norm) defined in the Lie algebra of $G$. From this we derive the distanc

Samuel J. Eschker, Antik Chakraborty, Melanie Gall, Peter Jevtic

Mortgage default rates, on the one hand, serve as a measure of economic health to support decision-making by insurance companies, and on the other hand, is a key risk factor in the asset-liability management (ALM) practice, as mortgage related assets constitute a significant proportion of insurers' investment portfolios. This paper studies the relationship b

Ehsan Amani, Mohammad Bagher Molaei, Morteza Ghorbani

Approximate Deconvolution (AD) has emerged as a promising closure for Large-Eddy Simulation (LES) in complex multi-physics flows, where the conventional pure Dynamic Eddy-Viscosity (DEV) models experience issues. In this research, we propose novel improved mixed hard-deconvolution or secondary-regularization models and compare their performance with the exis

Bikash Chandra Singh, Md Jakir Hossain, Rafael Diaz, Sandip Roy

The rapid growth of smart devices such as phones, wearables, IoT sensors, and connected vehicles has led to an explosion of continuous time series data that offers valuable insights in healthcare, transportation, and more. However, this surge raises significant privacy concerns, as sensitive patterns can reveal personal details. While traditional differentia

David Minkwan Kim, K. M. Brian Lee, Yong Hyeok Seo, Nikola Raicevic

We present the ongoing development of a robotic system for overhead work such as ceiling drilling. The hardware platform comprises a mobile base with a two-stage lift, on which a bimanual torso is mounted with a custom-designed drilling end effector and RGB-D cameras. To support teleoperation in dynamic environments with limited visibility, we use Gaussian s

Munazza K. Alam, Frederick Dauphin, Amanda Pagul

Here we describe a Jupyter notebook demonstrating methods for the reduction and analysis of exoplanet transit observations taken with the WFC3/UVIS G280 grism. Released on Space Telescope's hst_notebooks GitHub repository, this notebook presents an example workflow for processing time-series observations taken with the G280 grism - from the calibrated flat-f

Weiqin Chen, Nhan Huu Pham, Michael Robert Glass, Long Hai Vu

Reinforcement learning (RL) has demonstrated significant promise in enhancing the reasoning capabilities of Text2SQL LLMs, especially with advanced algorithms such as GRPO and DAPO. However, the performance of these methods is highly sensitive to the design of reward functions. Inappropriate rewards can lead to reward hacking, where models exploit loopholes

Elias Zimmermann

We show that for non-degenerate $k$-Markovian random fields with finite state space over a bounded degree graph with exponential growth rate $\theta$ uniform $\phi$-mixing with exponential decay rate $\lambda > 3\theta$ implies uniform $\psi$-mixing with exponential decay rate $(\lambda - 3\theta)/9$. As an application we obtain exponential $\psi$-mixing for

Thomas Gehrmann, Markus Löchner

Spin asymmetries in collisions of spin-polarized hadrons probe polarized parton distributions, which encode the spin structure of the colliding hadrons. To perform precision physics studies with spin asymmetries, higher order QCD corrections to the underlying polarized cross sections are required. Their numerical implementation relies on the use of an infrar

Omnilingual ASR team, Gil Keren, Artyom Kozhevnikov, Yen Meng

Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages behind. Expanding ASR coverage has been costly and limited by architectures that restrict language support, making extension inaccessible to most--all while entangled with ethical co

Xiang Xiang Wang, Sean Cottrell, Guo-Wei Wei

Single-cell data analysis seeks to characterize cellular heterogeneity based on high-dimensional gene expression profiles. Conventional approaches represent each cell as a vector in Euclidean space, which limits their ability to capture intrinsic correlations and multiscale geometric structures. We propose a multiscale framework based on Grassmann manifolds

Jordan D. Roberts, Vibodha Bandara, Kenichi Nakano, Dustin Keller

This paper presents the design, safety basis, and commissioning results of a 1 K liquid helium-4 (4He) evaporation refrigerator developed for the Fermilab SpinQuest Experiment (E1039). The system represents the first high power helium evaporation refrigerator operated in a fixed target scattering experiment at Fermilab and was engineered to comply with the F

Hiroshi Nakano, Hiroaki Nishi

Our previous work established the feasibility of FPGA-based real-time trajectory anonymization, a critical task for protecting user privacy in modern location-based services (LBS). However, that pioneering approach relied exclusively on shortest-path computations, which can fail to capture re- alistic travel behavior and thus reduce the utility of the anonym

E. Amani, A. Ahmadpour, M. J. Aghajari

Large Eddy Simulation (LES) of turbulent non-Newtonian flows involves two additional closures, namely the Non-Newtonian SubGrid-Scale (NNSGS) stress tensor and filtered viscosity. Here, dynamic closures are proposed for NNSGS, eliminating the need for model calibration. In addition, for the primary evaluation of LES closures, two canonical case studies are d

Isaac H. Goldstein, Julia A. Palacios

Coalescent models are used to study the transmission dynamics of rapidly evolving pathogens from molecular sequence data obtained from infected individuals. However coalescent parameters, such as effective population size, offer limited interpretability for transmission dynamics. In this work, we derive a coalescent model for exposed-infected population dyna

Harold Triedman, Alexios Mantzarlis

Elon Musk released Grokipedia on 27 October 2025 to provide an alternative to Wikipedia, the crowdsourced online encyclopedia. In this paper, we provide the first comprehensive analysis of Grokipedia and compare it to a dump of Wikipedia, with a focus on article similarity and citation practices. Although Grokipedia articles are much longer than their corres